An accurate assessment and identification of road pavement surfaces is required for timely maintenance of roads (pavements). Pavements develop many different modes of distresses over time, including but not limited to cracking, rutting, faulting, ponding, spalling and ravelling (i.e. on-going separation of aggregate particles in a pavement). The condition of the pavement can be determined by assessing the type, extent, relative and absolute location, and severity of each of these different types of distresses, and remedial measures can be applied to fix these problems. In addition, it is also important to measure the roughness and texture of pavements periodically. Textures helps to measure the skid resistance, and roughness measures the level of traveler comfort and impact on fuel efficiency.
Pavement surface conditions are usually assessed using survey vehicles which continually collect pavement surface data as they travel along their designated routes. A number of pavement condition assessment systems have been built in the past four decades. These systems use different sensors to digitize the road surface and roughly fall under one of the following two categories:                (1) Imaging systems, which use a camera or sets of cameras and lighting systems to record a view of the pavement surface. These systems usually use high resolution line scan cameras for accurate imaging. The individual lines scanned by the camera are stitched after some distance to get a two-dimensional image of the area scanned. They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection. However, these systems are two-dimensional (2D) as opposed to three-dimensional (3D).        (2) Profiling systems, which use laser triangulation, ultrasound or other time of flight sensors to record the elevation map of the pavement surface. These systems do not measure the entire surface of the road, but rather produce profiles at fixed intervals along a fixed number of lines on the road. While these systems are highly accurate and measure discrete points across the surface of the road, these systems take discrete measurements and therefore do not by their nature take images, as the 2D imaging systems described above do.        
The recorded road surface is then either assessed manually or automatically according to various pavement assessment standards.
Stereoscopy is the extraction of three dimensional (3D) elevation information from digital images obtained by imaging devices such as CCD and CMOS cameras. By comparing information about a scene from two vantage points, 3D information can be extracted by examination of the relative position of objects in the two panels. This is similar to the biological process Stereopsis, a process by which the human brain perceives the 3D structure of an object using visual information from two eyes.
In the simplest form of the technique, two cameras displaced horizontally from one another are used to obtain two differing views on a scene. By comparing these two images, the relative depth information can be obtained, in the form of disparities, which are inversely proportional to the differences in distance to the objects. To compare the images, the two views must be superimposed in a stereoscopic device or process.
For a two camera stereoscopic 3D extraction technique, the following steps are performed:                (a) Image Rectification: Transformation matrix Rrect transforms both the images to one common plane of comparison is identified. The left camera image is rectified by applying Rrect and the right camera image by applying R*Rrect to all the pixels.        (b) Disparity Map generation: For each pixel on the left camera image a matching pixel along the same scan line is identified on the right camera image using a localized window based search technique. For each pixel, pi(x,y) in the left image, the system and method identifies the matching pixel pr(x+d,y) in the right pixel where d is the pixel disparity.        (c) 3D reconstruction: At each point d(x,y) in the disparity map, the system and method calculates the elevation Z(x,y) by triangulation.        
Stereoscopy has been used for pavement quality assessment in U.S. Pat. No. 8,306,747. The system utilizes Ground Penetrating Radar (GPR) along with stereo area scan cameras to obtain high resolution images, and is not designed for operation at highway speeds. The system also does not use the image data directly for distress detection and measurement.
Techniques similar to multiple-camera stereoscopy like photometric stereoscopy has also been used in pavement assessment in Shalaby et al. (“Image Requirements for Three-Dimensional Measurements of Pavement Macrotexture”, Journal of the Transportation Research Board, Issue Volume 2068/2008, ISSN 0361-1981.) However, the system uses a conventional camera with four single point light sources, and is not designed for high-speed operation. The technique is used to characterize pavement surface textures.
Stereoscopic imaging has also been used for inspection of objects on a conveyor belt using both individual photo-sensors (U.S. Pat. No. 3,892,492) or using a line-scan camera (U.S. Pat. Nos. 6,166,393 and 6,327,374). They are also specifically designed to identify defective rapidly moving objects moving on a conveyor belt past a stationary sensor system, rather from a moving platform for road pavement evaluation.
What is therefore needed is an improved system and method for pavement scanning that overcomes some of the disadvantages of the prior art.